# AVALON：基于人类播种拓扑记忆的零样本室内导航

> 原文链接：https://robotics.hellobike.com/papers/2026-10-15-2610-21586
> 频道：papers | 更新日期：2026-10-15 | 标签：arxiv, cs.RO
> 核心摘要：AVALON面向机器人未见但用户熟悉的室内环境，以人类播种分层空间记忆实现拓扑导航，并在Unitree Go2任务上验证。

# AVALON：基于人类播种拓扑记忆的零样本室内导航

::: v-pre
> 原文链接：[AVALON: Zero-Shot Indoor Navigation with Human-Seeded Topological Memory](https://arxiv.org/abs/2610.21586)（arXiv 2610.21586 · 发布日期：2026-10-15）

**AVALON: Zero-Shot Indoor Navigation with Human-Seeded Topological Memory**

AVALON面向机器人未见但用户熟悉的室内环境，以人类播种分层空间记忆实现拓扑导航，并在Unitree Go2任务上验证。

## 核心要点

- 室内服务机器人常进入未建图建筑，但用户已了解布局与任务约束。
- 部署仅需平面图、地点线索和初始拓扑-里程计对齐，无需现场遍历或场地训练。
- 通过节点验证执行、重锚定和证据门控的人机记忆共更新，区分权限与可通行性。
- Go2实验中46.4秒5/5完成，较DimOS-Go2少40.9%机器人时间。

**验证范围与局限**：验证范围为一台Unitree Go2上的12.81米Intersection-to-Dining任务。

## 英文摘要 (Abstract)

Indoor service robots often enter unmapped buildings whose layouts and task constraints are already familiar to users. We address this human-familiar, robot-unseen setting with AVALON, a topological navigation framework organized around human-seeded layered spatial memory, node-verified topological execution, and task-triggered, evidence-gated human--robot memory co-updating. The robot has no prior on-site traversal or site-specific training. Deployment requires only a floor plan, place clues, and an initial topology-odometry alignment. Visual depth point-goal navigation leverages verified node arrivals to perform translational re-anchoring, without requiring a robot-constructed global SLAM map. Human--robot memory co-updating fuses complementary sources of information. Human operators specify task constraints that are unobservable via vision and report offline environmental modifications, whereas the robot provides physical-state evidence through active in-situ validation. Task relevance governs whether human-reported environmental changes undergo robot validation during the active mission. Field-collected perceptual evidence gates memory updates, maintaining an explicit separation between human-specified permissions and robot-observed traversability. Our experiments on a Unitree Go2 show that AVALON with X-NavDP completes a 12.81 m Intersection-to-Dining mission in 46.4 s (5/5) with no mapping, 40.9% less robot time than DimOS-Go2 (78.5 s, 4/5), while policy-only X-NavDP times out in all of the 5 runs. These demonstrate the superiority of the proposed topological navigation framework.

作者：Jing Xia, Changyan Li, Yuduo Wang, Guiyin Tian, Bo Wang, Yifan Liao, Mingjun Li, Shiyan Hu
:::

## 原始参考来源 (Sources)

- [arXiv 2610.21586：AVALON: Zero-Shot Indoor Navigation with Human-Seeded Topological Memory](https://arxiv.org/abs/2610.21586)